For MAML, the meta batch size was set to 5. The learning rate for the learner(fast weights) was initialized to 1e-3. The number of train steps for the learner(fast weights) were set to 1. The learning rate for the meta-learner was initialized to 1e-3 and set to halve every time the IoU plateaued for more than 8 epochs. Cosine annealing and One Cycle learning rate schedules were also tried but found to not significantly affect performance. AdamW[1]} optimizer was used for training the meta-learner. We found that the weight decay introduced by AdamW to be beneficial. These setting were same for FOMAML and Meta-SGD.
